Hello :)

Actually I'm doing my Master thesis and therefor I'm running a multilevel analysis with clustered data, but actually I get some warnings.

I have 103 participants and each of them answered up to 21 questionnaires (3 times daily). I have some missing data, but I ordered the data in the vertical format, so I'm loosing around 10% of the measurements.

If I run the null model for a binary outcome (nominal scale; 0,1 format), to check what proportion of the variance is caused by clustering of the data, the model is running but I get two warnings, and I'm not sure if I can ignore them.

I get the following warnings:

glmm: One or more records are not used in the analysis because they have one or more fields with invalid or missing values.

glmm: Valid values for events (target) and trials variables are non-negative and positive integers respectively, and the number of trials cannot be less than the number of events.

If I rerun the model after adding predictors the same warnings are displayed but the analysis is nevertheless running..

I use the following syntax for my model:

GENLINMIXED

 /DATA_STRUCTURE SUBJECTS=Person REPEATED_MEASURES=MP_timec COVARIANCE_TYPE=AR1

 /FIELDS TARGET=ND_Fortschritt_GMc_Cutoff TRIALS=NONE OFFSET=NONE

 /TARGET_OPTIONS DISTRIBUTION=BINOMIAL LINK=LOGIT

 /FIXED USE_INTERCEPT=TRUE

 /RANDOM USE_INTERCEPT=TRUE SUBJECTS=Person COVARIANCE_TYPE=VARIANCE_COMPONENTS SOLUTION=FALSE

 /BUILD_OPTIONS TARGET_CATEGORY_ORDER=DESCENDING INPUTS_CATEGORY_ORDER=DESCENDING

  MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=ROBUST PCONVERGE=0.000001(ABSOLUTE)

  SCORING=0 SINGULAR=0.000000000001

 /EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD.

I would be very grateful if somebody could help me with my question.

Thanks a lot!

Elisabeth

I am also having this exact same issue. Did you ever figure out how to resolve it?